feat: Set up comprehensive Python testing infrastructure

[llbbl]

Set Up Complete Python Testing Infrastructure

Summary

This PR establishes a comprehensive testing infrastructure for the NLP server project, providing a ready-to-use environment for developers to write and run tests effectively.

🏗️ Infrastructure Components Added

• Poetry Package Manager: Complete migration from requirements.txt to modern dependency management
• Testing Framework: pytest with coverage reporting, mocking utilities, and Flask testing support
• Directory Structure: Organized tests/ directory with unit/, integration/ subdirectories
• Configuration: Comprehensive pyproject.toml with testing, coverage, and code quality settings
• Shared Fixtures: Extensive conftest.py with mocks for NLP libraries and Flask app testing
• Validation Tools: Infrastructure validation script and sample tests

📋 Key Features

Testing Configuration:

• pytest with strict mode and comprehensive addopts
• Coverage reporting with 80% threshold
• HTML and XML coverage reports
• Custom markers: unit, integration, slow, api, requires_models
• Automatic test discovery and filtering

Mock Infrastructure:

• Pre-configured mocks for spaCy, gensim, polyglot, langid, and other NLP libraries
• Flask app and test client fixtures
• HTTP request mocking with newspaper and requests
• Consistent mock responses for reliable testing

Code Quality Tools:

• Black code formatting
• Flake8 linting
• MyPy type checking
• Comprehensive .gitignore covering testing artifacts

🚀 Running Tests

# Install dependencies
poetry install --with test

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov

# Run specific test categories
poetry run pytest -m unit          # Unit tests only
poetry run pytest -m integration   # Integration tests only
poetry run pytest -m "not slow"    # Skip slow tests

# Validate setup
python3 validate_setup.py

📁 Directory Structure

tests/
├── __init__.py
├── conftest.py              # Shared fixtures and utilities
├── test_infrastructure.py   # Infrastructure validation tests
├── unit/
│   ├── __init__.py
│   └── test_sample_unit.py  # Example unit tests
└── integration/
    ├── __init__.py
    └── test_sample_integration.py  # Example integration tests

🔧 Configuration Files

• pyproject.toml: Complete Poetry configuration with dependencies, testing, coverage, and code quality settings
• .gitignore: Updated with testing artifacts, virtual environments, and development files
• validate_setup.py: Standalone validation script for infrastructure verification

📝 Notes

• Dependency Management: Core NLP dependencies (spacy, gensim, etc.) have been moved to optional groups to prevent installation issues during testing setup
• Mock Strategy: Comprehensive mocking eliminates the need for downloading large ML models during testing
• Coverage Exclusions: Test files, virtual environments, and non-essential directories are properly excluded from coverage
• Flask Testing: Ready-to-use Flask test client with proper app configuration for API endpoint testing

✅ Validation

The infrastructure has been validated with:

• ✅ Directory structure verification
• ✅ Configuration file validation
• ✅ Mock fixture functionality testing
• ✅ pytest and coverage tool availability
• ✅ Basic assertion and mocking tests

🎯 Next Steps

Developers can now:

1. Write unit tests in tests/unit/ for individual functions
2. Create integration tests in tests/integration/ for API endpoints
3. Use the extensive fixture library for consistent mocking
4. Run poetry run pytest to execute tests with coverage reporting
5. Add new test markers as needed for test categorization

---

This testing infrastructure provides a solid foundation for maintaining code quality and reliability as the NLP server project grows and evolves.

🤖 Generated with Claude Code